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https://github.com/danny-avila/LibreChat.git
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* WIP(backend/api): custom endpoint * WIP(frontend/client): custom endpoint * chore: adjust typedefs for configs * refactor: use data-provider for cache keys and rename enums and custom endpoint for better clarity and compatibility * feat: loadYaml utility * refactor: rename back to from and proof-of-concept for creating schemas from user-defined defaults * refactor: remove custom endpoint from default endpointsConfig as it will be exclusively managed by yaml config * refactor(EndpointController): rename variables for clarity * feat: initial load custom config * feat(server/utils): add simple `isUserProvided` helper * chore(types): update TConfig type * refactor: remove custom endpoint handling from model services as will be handled by config, modularize fetching of models * feat: loadCustomConfig, loadConfigEndpoints, loadConfigModels * chore: reorganize server init imports, invoke loadCustomConfig * refactor(loadConfigEndpoints/Models): return each custom endpoint as standalone endpoint * refactor(Endpoint/ModelController): spread config values after default (temporary) * chore(client): fix type issues * WIP: first pass for multiple custom endpoints - add endpointType to Conversation schema - add update zod schemas for both convo/presets to allow non-EModelEndpoint value as endpoint (also using type assertion) - use `endpointType` value as `endpoint` where mapping to type is necessary using this field - use custom defined `endpoint` value and not type for mapping to modelsConfig - misc: add return type to `getDefaultEndpoint` - in `useNewConvo`, add the endpointType if it wasn't already added to conversation - EndpointsMenu: use user-defined endpoint name as Title in menu - TODO: custom icon via custom config, change unknown to robot icon * refactor(parseConvo): pass args as an object and change where used accordingly; chore: comment out 'create schema' code * chore: remove unused availableModels field in TConfig type * refactor(parseCompactConvo): pass args as an object and change where used accordingly * feat: chat through custom endpoint * chore(message/convoSchemas): avoid saving empty arrays * fix(BaseClient/saveMessageToDatabase): save endpointType * refactor(ChatRoute): show Spinner if endpointsQuery or modelsQuery are still loading, which is apparent with slow fetching of models/remote config on first serve * fix(useConversation): assign endpointType if it's missing * fix(SaveAsPreset): pass real endpoint and endpointType when saving Preset) * chore: recorganize types order for TConfig, add `iconURL` * feat: custom endpoint icon support: - use UnknownIcon in all icon contexts - add mistral and openrouter as known endpoints, and add their icons - iconURL support * fix(presetSchema): move endpointType to default schema definitions shared between convoSchema and defaults * refactor(Settings/OpenAI): remove legacy `isOpenAI` flag * fix(OpenAIClient): do not invoke abortCompletion on completion error * feat: add responseSender/label support for custom endpoints: - use defaultModelLabel field in endpointOption - add model defaults for custom endpoints in `getResponseSender` - add `useGetSender` hook which uses EndpointsQuery to determine `defaultModelLabel` - include defaultModelLabel from endpointConfig in custom endpoint client options - pass `endpointType` to `getResponseSender` * feat(OpenAIClient): use custom options from config file * refactor: rename `defaultModelLabel` to `modelDisplayLabel` * refactor(data-provider): separate concerns from `schemas` into `parsers`, `config`, and fix imports elsewhere * feat: `iconURL` and extract environment variables from custom endpoint config values * feat: custom config validation via zod schema, rename and move to `./projectRoot/librechat.yaml` * docs: custom config docs and examples * fix(OpenAIClient/mistral): mistral does not allow singular system message, also add `useChatCompletion` flag to use openai-node for title completions * fix(custom/initializeClient): extract env var and use `isUserProvided` function * Update librechat.example.yaml * feat(InputWithLabel): add className props, and forwardRef * fix(streamResponse): handle error edge case where either messages or convos query throws an error * fix(useSSE): handle errorHandler edge cases where error response is and is not properly formatted from API, especially when a conversationId is not yet provided, which ensures stream is properly closed on error * feat: user_provided keys for custom endpoints * fix(config/endpointSchema): do not allow default endpoint values in custom endpoint `name` * feat(loadConfigModels): extract env variables and optimize fetching models * feat: support custom endpoint iconURL for messages and Nav * feat(OpenAIClient): add/dropParams support * docs: update docs with default params, add/dropParams, and notes to use config file instead of `OPENAI_REVERSE_PROXY` * docs: update docs with additional notes * feat(maxTokensMap): add mistral models (32k context) * docs: update openrouter notes * Update ai_setup.md * docs(custom_config): add table of contents and fix note about custom name * docs(custom_config): reorder ToC * Update custom_config.md * Add note about `max_tokens` field in custom_config.md
161 lines
4.3 KiB
JavaScript
161 lines
4.3 KiB
JavaScript
const { EModelEndpoint } = require('librechat-data-provider');
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const models = [
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'text-davinci-003',
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'text-davinci-002',
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'text-davinci-001',
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'text-curie-001',
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'text-babbage-001',
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'text-ada-001',
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'davinci',
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'curie',
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'babbage',
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'ada',
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'code-davinci-002',
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'code-davinci-001',
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'code-cushman-002',
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'code-cushman-001',
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'davinci-codex',
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'cushman-codex',
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'text-davinci-edit-001',
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'code-davinci-edit-001',
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'text-embedding-ada-002',
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'text-similarity-davinci-001',
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'text-similarity-curie-001',
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'text-similarity-babbage-001',
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'text-similarity-ada-001',
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'text-search-davinci-doc-001',
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'text-search-curie-doc-001',
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'text-search-babbage-doc-001',
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'text-search-ada-doc-001',
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'code-search-babbage-code-001',
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'code-search-ada-code-001',
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'gpt2',
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'gpt-4',
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'gpt-4-0314',
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'gpt-4-32k',
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'gpt-4-32k-0314',
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'gpt-3.5-turbo',
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'gpt-3.5-turbo-0301',
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];
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const openAIModels = {
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'gpt-4': 8191,
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'gpt-4-0613': 8191,
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'gpt-4-32k': 32767,
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'gpt-4-32k-0314': 32767,
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'gpt-4-32k-0613': 32767,
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'gpt-3.5-turbo': 4095,
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'gpt-3.5-turbo-0613': 4095,
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'gpt-3.5-turbo-0301': 4095,
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'gpt-3.5-turbo-16k': 15999,
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'gpt-3.5-turbo-16k-0613': 15999,
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'gpt-3.5-turbo-1106': 16380, // -5 from max
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'gpt-4-1106': 127995, // -5 from max
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'mistral-': 31995, // -5 from max
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};
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// Order is important here: by model series and context size (gpt-4 then gpt-3, ascending)
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const maxTokensMap = {
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[EModelEndpoint.openAI]: openAIModels,
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[EModelEndpoint.custom]: openAIModels,
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[EModelEndpoint.google]: {
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/* Max I/O is combined so we subtract the amount from max response tokens for actual total */
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gemini: 32750, // -10 from max
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'text-bison-32k': 32758, // -10 from max
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'chat-bison-32k': 32758, // -10 from max
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'code-bison-32k': 32758, // -10 from max
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'codechat-bison-32k': 32758,
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/* Codey, -5 from max: 6144 */
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'code-': 6139,
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'codechat-': 6139,
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/* PaLM2, -5 from max: 8192 */
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'text-': 8187,
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'chat-': 8187,
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},
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[EModelEndpoint.anthropic]: {
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'claude-2.1': 200000,
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'claude-': 100000,
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},
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};
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/**
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* Retrieves the maximum tokens for a given model name. If the exact model name isn't found,
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* it searches for partial matches within the model name, checking keys in reverse order.
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*
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* @param {string} modelName - The name of the model to look up.
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* @param {string} endpoint - The endpoint (default is 'openAI').
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* @returns {number|undefined} The maximum tokens for the given model or undefined if no match is found.
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*
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* @example
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* getModelMaxTokens('gpt-4-32k-0613'); // Returns 32767
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* getModelMaxTokens('gpt-4-32k-unknown'); // Returns 32767
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* getModelMaxTokens('unknown-model'); // Returns undefined
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*/
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function getModelMaxTokens(modelName, endpoint = EModelEndpoint.openAI) {
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if (typeof modelName !== 'string') {
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return undefined;
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}
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const tokensMap = maxTokensMap[endpoint];
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if (!tokensMap) {
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return undefined;
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}
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if (tokensMap[modelName]) {
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return tokensMap[modelName];
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}
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const keys = Object.keys(tokensMap);
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for (let i = keys.length - 1; i >= 0; i--) {
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if (modelName.includes(keys[i])) {
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return tokensMap[keys[i]];
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}
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}
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return undefined;
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}
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/**
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* Retrieves the model name key for a given model name input. If the exact model name isn't found,
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* it searches for partial matches within the model name, checking keys in reverse order.
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*
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* @param {string} modelName - The name of the model to look up.
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* @param {string} endpoint - The endpoint (default is 'openAI').
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* @returns {string|undefined} The model name key for the given model; returns input if no match is found and is string.
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*
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* @example
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* matchModelName('gpt-4-32k-0613'); // Returns 'gpt-4-32k-0613'
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* matchModelName('gpt-4-32k-unknown'); // Returns 'gpt-4-32k'
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* matchModelName('unknown-model'); // Returns undefined
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*/
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function matchModelName(modelName, endpoint = EModelEndpoint.openAI) {
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if (typeof modelName !== 'string') {
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return undefined;
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}
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const tokensMap = maxTokensMap[endpoint];
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if (!tokensMap) {
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return modelName;
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}
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if (tokensMap[modelName]) {
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return modelName;
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}
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const keys = Object.keys(tokensMap);
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for (let i = keys.length - 1; i >= 0; i--) {
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if (modelName.includes(keys[i])) {
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return keys[i];
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}
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}
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return modelName;
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}
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module.exports = {
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tiktokenModels: new Set(models),
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maxTokensMap,
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getModelMaxTokens,
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matchModelName,
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};
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